Process sensors are used in a wide range of industrial process control applications. A process sensor or transmitter is a device having one or more transducers and electronics that convert transducer signals into a measurement value recognizable by an associated process control or monitoring system. The measurement value may be used as a process variable by the process control system. Increasingly, local computing power has been used to carry out internal diagnostics within “intelligent” sensors such as self validating (SEVA™) process sensors. A SEVA™ process sensor is a type of intelligent process sensor that performs additional processing to generate information including generic validity metrics for each measurement produced by the sensor. The metrics generated by a SEVA™ process sensor include, for example, a validated measurement value (VMV), a validated uncertainty (VU) of the measurement value, and a measurement value status (MV status). These SEVA™ process metrics represent the quality and confidence for each measurement produced by the process sensor. Additional description of the SEVA™ standard can be found in British Standard BS7986:2001, titled Specifications for Data Quality Metrics for Industrial Measurement and Control Systems, which is incorporated herein by reference.
Specifically, the validated measurement value (VMV) is the SEVA™ process sensor's best estimate of the true measurand value of the process variable, taking all diagnostic information into account. If a fault occurs, then the VMV can be corrected to the best ability of the SEVA™ sensor, and additional information can be generated by the sensor to alert the process control system of the fault. In the most severe cases, such as where the raw data is judged to have no correlation with the measurand, the VMV can be extrapolated entirely from past measurement behavior.
The validated uncertainty (VU) is the uncertainty associated with the VMV. The VU gives a confidence interval for the true value of the measurand. For example, if VMV as determined by the process sensor is 2.51 units, and the VU is 0.08, then there is a 95% chance that the true measurement lies within the interval 2.51±0.08 units. The VU takes into account all likely sources of error, including noise, measurement technology and any fault-correction strategy being used by the process sensor. When a fault is detected, the SEVA™ sensor has the ability to correct the VMV and increase the VU to account for the reduction in the confidence of the reading.
The measurement value status (MV status) is a discrete-valued flag indicating how the VMV has been calculated by the process sensor. The MV status also reflects the presence and the persistence of any process sensor fault. The MV status assists users (whether human or automated) to determine whether the measurement is acceptable for use in a particular application. For example, measurement data given a BLIND status should never be used for feedback control, as BLIND data is projected from past measurement value history and will not respond to the actions of feedback control.
The MV status flag generated by the process sensor can take on any one of a set of discrete values. For example, possible values for the MV status flag in one implementation include CLEAR, BLURRED, DAZZLED, BLIND, SECURE DIVERSE, SECURE COMMON, and UNVALIDATED. When the MV status is CLEAR, confidence in the raw measurement is nominal, and the VMV is generated purely from the current raw measurement. When the MV status is BLURRED, a fault that impairs measuring capability has been diagnosed, and the VMV is generated by compensating the current raw measurement. When the MV status is DAZZLED, the raw measurement is substantially abnormal and the confidence associated with it is zero, but the fault is judged to be temporary, such as during a transient period. During this condition, the VMV is generated from the sensor's measurement value history associated with the device. When the MV status is BLIND, a fault that destroys the measuring capability of the process sensor has been diagnosed, and there is no confidence in the raw measurement. During this condition, the VMV is generated from the sensor's measurement history associated with the device. When the MV status is SECURE DIVERSE or SECURE COMMON, the VMV is obtained by combining redundant SEVA™ measurements, and the confidence in each SEVA™ measurement is nominal. When the MV status is UNVALIDATED, validation within the SEVA™ process sensor is not currently taking place.
An automated process control system in an industrial processing system may receive process variables as measurement values from a variety of sensors and actuators that monitor and assist in the operation of the industrial processing system. The process variables are generated by process sensors or transmitters that transmit the process variables to the process control system over a communication channel or network. A variety of communication approaches currently exist for transmitting the process variables. These approaches range from low bandwidth analog communication channels, such as analog, pulse, alarm status, and 4–20 mA, to higher bandwidth digital communication channels, such as fieldbus or the FoxCom communication protocol available from Invensys Systems, Inc. Currently there exist many installed process control systems that receive process variable information (as feedback) generated by process sensors connected to low bandwidth communication channels. These systems typically use non-SEVA™ sensors and are limited in the amount of process variable information that can be communicated over the low bandwidth network from the process sensors to the process control system. For example, some process control systems are only capable of receiving binary input information such as the state of an alarm signal being on or off, or a 4–20 mA signal representing the measured process variable. Therefore, these low bandwidth systems are typically not capable of communicating the higher bandwidth process variable information associated with a digital SEVA™ process sensor. Moreover, many existing automated process control systems are not able to process the metrics generated by a SEVA™ process sensor, and merely rely upon non-SEVA™ sensors generating alarm signals when faults occur in the industrial processing system.
In the absence of localized process variable validation (such as through an intelligent SEVA™ process sensor), measurement redundancy has been used to ensure that a verified and reliable measurement of the process variable is provided to the process control system with high availability, such that a fault or failure associated with one process sensor doesn't result in complete loss of the measurement to the process control system. Such redundancy may be implemented through the use of several independent sensors that monitor the same process variable, usually termed hardware redundancy, or through a plant model that provides an independent estimate of the process variable, usually termed analytical redundancy.